To take advantage of recent and ongoing advances in intensive and large-scale computational methods, and to preserve the scientific data created by publicly funded research projects, data archives must be created as well as standards for specifying, identifying, and annotating deposited data. The value of and interest in such archives among researchers can be greatly increased by adding to them an active computational capability and framework of analysis and search tools that support further analysis as well as larger scale meta-analysis and large scale data mining. The OpenNeuro.org archive, begun as a repository for functional magnetic resonance imaging (fMRI) data, is such an archive. We propose to build a gateway to OpenNeuro for human electrophysiology data (EEG and MEG, as well as intracranial data recorded from clinical patients to plan brain surgeries or other therapies) ? herein we refer to these modalities as neuroelectromagnetic (NEM) data. The Neuroelectromagnetic Data Archive and Tools Resource (NEMAR) at the San Diego Supercomputer Center will act as a gateway to OpenNeuro for NEM data research. Such data uploaded to NEMAR at SDSC will be deposited in the OpenNeuro archive. Still- private NEM data in OpenNeuro will, on user request, be copied to the NEMAR gateway for further user processing using the XSEDE high-performance resources at SDSC in conjunction with The Neuroscience Gateway (nsgportal.org), a freely available and easy to use portal to use of high-performance computing resources for neuroscience research. Publicly available OpenNeuro NEM data will be able to be analyzed by running verified analysis applications on the OpenNeuro system. In this project we will build an application to evaluate the quality of uploaded NEM data, and another to visualize the data, for EEG and MEG at both the scalp and brain source levels, including time-domain and frequency-domain dynamics time locked to sets of experimental events learned from the BIDS- and HED-formatted data annotations. The NEMAR gateway will take a major step toward applying machine learning methods to a large store of carefully collected and stored human electrophysiologic brain data to spur new developments in basic and clinical brain research.